Monday, March 31, 2014

My Drive

Saturday, March 29, 2014

Research on Research

Here is research on a game of research:

We formulated an Academic Game, where an authors' current h-index is the total effort/credit they have to invest in their research collaborations, and they can split that wisely to maximize their future h-index returns. We need to analyze this game some more!

Something to be modeled. I call this the Sheen Problem.

A Harvard faculty imparts some sheen to Harvard based on their research accomplishments or awards, and in turn, Harvard confers some gleam on them via its stature, history, alumni and so on. Informally, I want to separate Harvard's component from other components in the stature of a Harvard faculty. Is there a suitable model to study this? Jon Kleinberg-esque progress will be cool. (For example, one can focus on the h-index of a Harvard faculty and find a way to discount it for the Harvard effect. There are potentially other approaches.)

Friday, March 14, 2014

Travels and Incredibly X India

It is hard NOT to be an observer when you travel and when you observe, it is hard NOT to be a writer (not a photographer: privacy issues; not a painter: you need tools).

There is an ad for Oracle Exadata which is basically a picture of a metal box. This is the best ad one can create?

I watched the movie And Who Taught You To Drive? on the plane, a movie about three expats (german, american and korean) trying to get a drivers license in foreign countries (india, japan and germany, resp). It is an interesting exercise in teaching and other idiosyncracies of cultures, who knew getting a drivers license was so hard, and the strange part is who succeeds (no spoilers).

There is a tourism ad campaign for India called, Incredible India. There might even be an exclamation point somewhere. I think there are missing adjectives. Incredibly colorful/indelible/insane/.. India. I would like to see Incredibly Clean India. There is an outstanding question. In the 5k+ yrs of Indian civilization, when did the local population manage to keep public spaces clean?

Big Data Call

There is an NSF solicitation out for Big Data with a deadline of June 9. It has emphasis on Foundations, so beyond the fact that the theory community is made for Big Data (I mean, we improve algorithms by looking at instances as n \rightarrow \infty), this seems even more on target. Simons theory center at Berkeley had their Big Data program, I blogged about the Big Data panel in database theory, and there are many, many other efforts to stimulate discussions and identify challenges in Big Data theory (one in IISc Bangalore is interesting). So, this solicitation seems like a place to propose concrete next or long term steps for research. As an aside, check out discussions on "urban data" from NYC and related efforts. Pretty viz.

Analyzing publications

I have an interest in analyzing publications. In prior work, we looked at clues authors leave in the latex source of papers (not PDFs). We extended that to study if there is a bias how one or the other authors' names is used to refer to a joint publication. Beyond that, we got curious about a natural challenge. We write reference letters for other researchers and while recommending Alice, we need to find Bob and Carol who are suitable, comparable researchers. This is not a trivial task. Sometimes one looks across areas and other times within; sometimes one looks for someone with similar experience and other times with a similar initial trajectory so we can project out by analogy; sometimes one is comparable in volume, other times by citations and quality; etc. We did a simple study recently to see if one can automatically/algorithmically find comparable researchers. Here is the paper, we found some examples that challenged us to explain them, please email me if you want to find out more.

Anyway, this is the backdrop to what I really wanted to blog (v):

Once in a while we get rankings of universities (ex). These cause angst or jubilation. On the other hand, there is a lot of theory of rankings, axiomatic, algorithmic, strategic and so on that point to the challenge of producing defensible rankings. Going beyond, are there interesting research ideas to automatically/algorithmically produce university rankings? I would love to see some research that even tackles a narrower problem of ranking departments based on the publications of the current faculty, or ranking based on the graduated PhDs, etc.